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Article
Publication date: 27 June 2008

Thomas Gulledge and Tamer Chavusholu

This paper aims to automate the supply chain operations reference (SCOR) model as an enabler for process‐oriented supply chain business intelligence.

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Abstract

Purpose

This paper aims to automate the supply chain operations reference (SCOR) model as an enabler for process‐oriented supply chain business intelligence.

Design/methodology/approach

The hypothesis is the following: SCOR model automation is possible using data that is directly extracted from integrated enterprise systems. To test the hypothesis, an alignment product that allows the SCOR model to be automated with information that is directly extracted from the Oracle E‐Business Suite was developed.

Findings

In order to achieve the full benefits from the SCOR model, effective business process management and the SCOR key performance indicators (KPIs) must be implemented and used. Unless data collection to support KPI construction is automated, it is difficult to institutionalize the SCOR model as a measurement and benchmarking framework. We have demonstrated that automated support for KPIs is feasible and achievable.

Research limitations/implications

The E‐Business Suite is a single enterprise solution, but we assert that the same procedures could be followed with other enterprise solutions or even applied in a legacy system environment.

Originality/value

The developed solution described in the paper can immediately be applied to the design, development, and deployment of corporate performance management systems.

Details

Industrial Management & Data Systems, vol. 108 no. 6
Type: Research Article
ISSN: 0263-5577

Keywords

Article
Publication date: 6 February 2017

Bhupender Singh, Sandeep Grover and Vikram Singh

The purpose of this paper is to generate awareness of contributions made by benchmarking toward building performance of Indian service industries in globally market…

Abstract

Purpose

The purpose of this paper is to generate awareness of contributions made by benchmarking toward building performance of Indian service industries in globally market. Ranking of Benchmarking is done on the basis of their application which give confidence for the managers to adopt in their Industries so that they may become best in their field.

Design/methodology/approach

Methodology consists of three phase: define, phase include definitions, factors of benchmarking as literature outcomes, questionnaire survey and outcome of survey. In the second phase, analysis of collected data and applications of multi-criteria decision-making approaches [technique for order preference by similarity to ideal solution (TOPSIS) and analytical network process (ANP)] are used. The last phase includes comparison of results which gives validation in similarities of ranking obtained.

Findings

The study identifies seven different benchmarking techniques used for service industries. Using TOPSIS and ANP approaches shows similarity that external benchmarking, performance benchmarking and internal benchmarking are the first three ranks that give basis for several critical success factors s, namely, planning, reliability, standardization, time behavior, usability, etc., as part of benchmarking using in service industries.

Research limitations/implications

The limitation is the assumptions made by multi-criteria decision-making approaches which may effect the analysis of the study as these are taken theoretically.

Originality/value

This study is a first attempt to find similarities in both techniques while comparing benchmarking in Indian service industries.

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